The performance monitoring of control loops is used in industrial plants, after the control loops have been configured and set into operation, and it is a means to detect any malfunctioning of the elements and components inside the control loop based on normal operational data of the control loop. In other words, performance monitoring is an online or offline diagnosis of the quality of control loop operation. As a result of the performance monitoring, it can be ensured that the sensors, actuators and the industrial processing devices which control the operation of the actuators based on the measurements taken by the sensors, as well as any networking components which interconnect the sensors, actuators and industrial processing devices are all working properly.
In general, performance monitoring is performed by surveying different kinds of statistics which reflect the control performance over time and by detecting any abnormalities in these statistics. An introduction into the field of control performance monitoring is for example given in a document by Mohieddine Jelali, “An overview of control performance assessment technology and industrial applications”, Control Engineering Practice 14 (2006), pp. 441-466.
ABB markets a known software package under the name Loop Performance Manager (LPM). It provides a solution for the optimisation and maintenance of basic control loops of industrial plants and includes a tool for the automatic evaluation of the performance of the control loops, called loop auditing. The basic concepts behind the loop auditing tool are described for example in Bonavita et al, “Control loops: performance and diagnostics”, presented at 48th ANIPLA Conference, Milan, 14-15, Sep. 2004.
When the performance of a control loop is being evaluated using statistical methods, as is for example done in the loop auditing tool of LPM, there is normally no mathematical model available of the process being controlled, and the disturbances acting on the process are unknown. Therefore, it is attempted to estimate the performance of the control loop by assuming that the control loop possesses properties that are characteristic to the control loop type in question, for example pressure control, temperature control, etc. Accordingly, LPM permits the grouping of control loops into categories. Thereby, the following characteristics of the control loop are predefined: the representative time constants of the process controlled by the control loop category (e.g., type) in question, the representative disturbances, and, based on the previous, the representative response of the control loop. Based on these characteristics, the sampling frequency and the number and length of data batches, e.g., of periodical process data to be captured, are predefined as well.
Depending on the type of control loop to be monitored, the loop auditing tool of LPM performs the specified number of collections of process data at predefined times, while operating in background so that the normal control loop operation remains undisturbed. From the collected process data, the programme calculates different performance indices, also called key performance indices (KPI), some of which are calculated in continuous mode for each newly taken data sample and most of which are calculated in batches, e.g., at the end of the data collection.
The control loop categories are characterized by threshold values, which are defined for the different statistical and other performance indices. In order to generate clear diagnostic suggestions with respect to the performance of the control loop, the performance indices are each compared to its corresponding threshold value. Based on the result of each individual comparison, a value indicating the quality of the control performance with respect to the specific performance index is calculated, where these values are called sub-qualities. Since the performance indices stand for different aspects of the behaviour of the control loop, their corresponding threshold values need to be chosen individually in order to take into account that some indices reflect an improved control performance by having an increased value and some reflect an improved control performance by having a decreased value. By selecting the type of control loop in question out of the above mentioned predefined fixed set of control loop types, an adjustment of the threshold values up to a certain degree is performed.
From the sub-qualities, an overall quality value for the control performance of the control loop in question is derived by weighing each sub-quality and by calculating the sum of the weighted sub-qualities. The overall quality, in the following also called first quality, of the control performance is then expressed by translating the quality value into one of four self-explaining performance categories: excellent, good, fair or poor.
Even though selecting the control loop type works sufficiently well in a majority of cases, situations may occur where a control loop is diagnosed as poor or, more generally, as less than good, despite having selected the correct control type and having configured and tuned the control loop properly. This condition is due to uncommon background conditions which are not reflected in the assumed disturbances and the assumed representative behaviour of the control loop. For example, the industrial plant may be subject to a continual disturbance, to which the control loop is able to react so that the disturbance does not affect the operation of the industrial plant. However, the continual disturbance may superimpose some of the measured process data which influences the outcome of the performance diagnosis. An erroneous result of the control performance monitoring may have unwanted and negative impacts, such as the distraction of a user which supervises the operation of the industrial plant from correct and important error and alarm messages. Further, false alarms could cause extra costs due to fruitless assignments of service and maintenance personnel.